Co-opetition Index Based on Rival Behavior in Social Networks

ABSTRACT

An approach is provided that creates a co-opetition index. The co-opetition index is created by mining data from online sources that are related to competitor activities associated with a competitor of an organization. The competitor is selected from many competitors of the organization. Possible business actions are automatically identified that correspond to the competitor activities. The set of possible business actions are analyzed using a game theory analysis. The game theory analysis results in an identification of next actions that maximize a payoff to the organization. The co-opetition index is adjusted based on a classification of the next actions on a competitiveness scale. The resulting co-opetition index is then provided to a user of the system.

BACKGROUND

In current times, co-opetition is a common behavior seen in commercialenterprises. Co-opetition is loosely defined as a cooperativecompetition. In a co-opetitive environment, enterprises take intoaccount cooperative and competitive behavior in aggregate to makeoptimal strategic decisions. Typical use cases include a company makinga splashy announcement e.g. bigger and better than what their rivals hadtweeted and yet not affect cooperation in other unrelated areas whererivals engage in cooperative research. Other examples include brandsoffering more lucrative discounts than what their competitors hadoffered in order to win over consumers based on a blog post they made,opposing industries offering better services in a specific area due tonegative feedback on social networks from residents, politicalopposition beating the rival to that blue shirt campaign, etc. Achallenge with current co-opetition is that decisions are often made on“gut feel” or intuition with little or no numeric analysis regardingactions and their effect on co-opetition.

Presently social networks are focused on identifying people/topics youwould want to be socially interactive with (e.g., friends, colleagues,etc.) but these relationships do not take into account rivals over whichyou may want to gain a competitive advantage. It is difficult to keeptrack of rivals (since there is no friend relationship), and it is alsodifficult to keep track of changing relationships of former friendsturning into rivals. Social media analytics and data mining techniquesare used for information retrieval, statistical modeling and machinelearning. These techniques employ data pre-processing, data analysis,and data interpretation processes in the course of data analysis. Thesesolutions can be expensive, and might require employment of datascientists and knowing entities, attributes, patterns and data access inorder to obtain useful analysis. In addition, some businesses may onlyrun this type of analysis at set times or obtain the information througha 3rd party which could be too late to gain the competitive advantageover a competitor if such opportunity has already passed.

BRIEF SUMMARY

An approach is provided that creates a co-opetition index. Theco-opetition index is created by mining data from online sources thatare related to competitor activities associated with a competitor of anorganization. The competitor is selected from many competitors of theorganization. Possible business actions are automatically identifiedthat correspond to the competitor activities. The set of possiblebusiness actions are analyzed using a game theory analysis. The gametheory analysis results in an identification of next actions thatmaximize a payoff to the organization. The co-opetition index isadjusted based on a classification of the next actions on acompetitiveness scale. The resulting co-opetition index is then providedto a user of the system.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present disclosure,as defined solely by the claims, will become apparent in thenon-limiting detailed description set forth below.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosure may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion answering (QA) system in a computer network;

FIG. 2 illustrates an information handling system, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein;

FIG. 3 is an exemplary diagram depicting the components utilized increating a co-opetition index based on rival behavior in socialnetworks;

FIG. 4 is an exemplary high-level flowchart that performs steps tocreate a co-opetition index based on rival behavior in social networks;

FIG. 5 is an exemplary flowchart that performs steps to perform acompetitor analysis and identification module;

FIG. 6 is an exemplary flowchart that performs steps to perform anactivity analyzer module of competitor activities; and

FIG. 7 is an exemplary flowchart that performs steps to perform anactivity significance module based on the identified and analyzedcompetitor activities.

DETAILED DESCRIPTION

FIGS. 1-7 depict an approach that creates a co-opetition index based onrival behavior in social networks. In the approach, a co-opetition indexis created based on rival behavior as found in online sources, such associal networks. The approach includes a friendship/rival co-opetitionscale (algorithm) that can be set. For example, the index can be a scalevalue from 1 to 10 with 1 being a business partner (friend) and 10 beingmajor competitor (foe). The other increments (2 to 9) being set toincrease or decrease the relationship strength. The index can also bechanged over time. A second component allows for specific entity(organizations, individuals, pages, etc.) to be added (knownrivals/friends list) so the approach takes these into account as part ofpattern analysis. A third component allows for listing keywords thatshould be included in the pattern analysis engine. The pattern analysisengine uses the information to mine online data (e.g., posts, “likes,”feeds, comments, announcements, etc.) in relation to the scale inaddition to social network analysis will learn over time when to notifydecision makers of situations that could gain the opposition anadvantage or opportunity.

The pattern analysis engine uses Bayesian Game Theory payoff functionsto increase or decrease the co-opetition scale. In addition, theapproach automatically adjusts the scales and keyword components.Similar to social media friend suggestions, the engine also recommendsrival/competitor suggestions to add to an organization's “watch list”based on pattern analysis and rival relationships. The scale canautomatically be adjusted by the engine given new information. Forexample, when a known business partner (friend) posts a negative commentor becomes friends with a known rival, thereby automatically increasingthe co-petitive index scale (e.g., from 1 to 3, etc.) and notifies adecision maker of the change in the business partner. The notificationcomponent is responsible for sending messages as information becomesavailable using either email, SMS or Push notifications.

The co-petitive index approach described herein provides an organizationwith various competitive advantages. First, the organization is notifiedof a rival's social activity that can be used to gain a competitiveadvantage. Second, the organization is notified of new potential rivalseither through pattern analysis or relationships to existing rivals(e.g. new startups, etc.). Third, the organization is notified whenfriends/business partners forge relationships with known rivals movingthem to your “watch list” and changing their co-petitive index value. Inaddition, the approach provides cost savings to the organization as itavoids the need for costly social media analytic solutions, datascientists etc.

The co-opetition scale component is used to define a rivals strengthusing an algorithm between linked entities (e.g., 1 being a businesspartner (friend) to 10 being a major competitor (foe). This scale can beadjusted to increase the relationship strength using increments 2through 9. The higher the rival strength, the more analysis and contentwill be analyzed through the pattern analysis engine based on BayesianGame Theory. The known entity component is used to define known entitiessuch as organizations, individuals, specific pages to monitor, groupsites, etc. When these entities are added, the organization can settheir scale size in relation to the rival scale component. The keywordcomponent is used to define a list of keywords that the organizationwants the pattern analysis engine to take into account during theanalysis. The keyword component is used to find other rivals by usingthese keywords in the analysis.

The pattern analysis engine is responsible for performing severalfunctions. These functions include mining the data from online sources,such as social networks, using the co-petitive index scaling algorithm,entity and keyword components and applying advanced social mediaanalytics and data mining techniques to identify relevant content. Thepattern analysis engine also identifies new rivals from the keywordanalysis and adjusts existing rival relationships. The pattern analysisengine further identifies existing friendly/partner relationships thathave been forged an associations with existing rivals. The patternanalysis engine passes the data on to the relevance component foradjusting the co-opetition scale.

The relevance component is responsible for performing several functionsthat include determining whether the information has relevance inrelation to decisions made on prior content (e.g., show more ofthis/less of this concept, etc.) to avoid unnecessary information frombeing sent. The relevance component also automatically adjusts theco-opetition scale component for entities that have new associationswith existing rivals (e.g., adding entities to the “watch list” usingthe output of a payoff function for each rival, etc.). The relevancecomponent further stores decisions made based on content and updates thepattern analysis engine accordingly. The relevance component sends validcontent to the notification component and grades, or scales, therelevance based on a threshold level to generate alerts dynamically witha measure of criticality. The relevance factor is computed based on thetype of activity of the rival and how it impacts own strategies. Therelevance factor can be positive or negative. A positive relevancefactor indicates a rival activity that benefits us (e.g., a skilledplayer leaves the rival team, the rival company loses an order, etc.). Anegative relevance factor, such as the rival sports team appointing anew coach or receiving additional media coverage. The notificationcomponent is used to send content through multiple channels such asemail, SMS or Push notifications to decision makers in the organization.

The pattern analysis performs various steps in both the CompetitorAnalyzer/Identifier and the Competitive Activity Analyzer components.Steps performed by the pattern analysis include analyzing events, suchas announcements or comments or profile changes, from online sourcessuch as social network sources, from known entities as well as relatedentities against the keywords established by the organization. For eachevent, the pattern analysis automatically determine a set of probablenext actions and corresponding payoffs (e.g., a counter announcement ora new partnership, etc.). The pattern analysis performs a Bayesian gametheory analysis to determine which next action will maximize the payoffto the organization. In one embodiment, the payoff is maximized when inthe co-opetition index of the rival sits in the middle of the scale(e.g., a 5 in a 10 point scale, etc.). Each next action is classifiedinto a competitive action or a collaborative action. Depending on theclass of the next action, the rival's co-petitive index is adjusted(e.g., either positively if it is a competitive action or negatively ifit is a friendly action, etc.). The organization creates a profile ofthe organization with interests, activities, areas of interest, actionplans, etc.

The Competitor Analyzer/Identifier module uses the organization'sprofile information and mines the online data, such as social mediadata, to identify potential Rivals/Competitors. The analysis isorthogonal to the friends recommendation system prevalent in industry.This identification is also based on “conflict of interests” such as in“rival sports teams”, “competing businesses”, “rival political parties”,and may include such areas as “competing mass media channels,” etc. Adynamic list of “competitors” is maintained in a repository and updatedcontinuously. The organization can also provide a list of “competitors”to this list as a supplement to the list or as seed information whengenerating the list. Once the list of competitors is available, the“Activity Analyzer” identifies information that may be interesting tothe organization. In one embodiment, this is the opposite of identifying“common interests” used by traditional recommendation systems. Unusualactivities, distinct approaches (e.g. a rival political party is buyinga specific TV channel air time, a competing sports team recruiting a newcoach, a rival business expanding coverage of a new set of products,opening a new office or branch in a new location, announcement of a saleor rebates, participating in a special event, launching anew initiative,an acquisition rumor, new market wins/losses, etc. In one embodiment,the activity significance is computed based on potential adversaryeffects, unusual nature, even unexpected or unknown impact events, whichneed to be monitored or tracked. The activities of anadversary/rival/competitor is often compared with the organization's ownactivities, plans, and expectations, etc. Appropriate alerts reports canbe produced by the system based on threshold(s) or objective functionsset by the organization's decision makers.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions. The following detailed description willgenerally follow the summary of the disclosure, as set forth above,further explaining and expanding the definitions of the various aspectsand embodiments of the disclosure as necessary.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system 100 in a computer network 102. QA system 100may include knowledge manager 104, which comprises one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like. Computer network 102may include other computing devices in communication with each other andwith other devices or components via one or more wired and/or wirelessdata communication links, where each communication link may comprise oneor more of wires, routers, switches, transmitters, receivers, or thelike. QA system 100 and network 102 may enable question/answer (QA)generation functionality for one or more content users. Otherembodiments may include QA system 100 interacting with components,systems, sub-systems, and/or devices other than those depicted herein.

QA system 100 may receive inputs from various sources. For example, QAsystem 100 may receive input from the network 102, a corpus ofelectronic documents 107 or other data, semantic data 108, and otherpossible sources of input. In one embodiment, some or all of the inputsto QA system 100 route through the network 102 and stored in knowledgebase 106. The various computing devices on the network 102 may includeaccess points for content creators and content users. Some of thecomputing devices may include devices for a database storing the corpusof data. The network 102 may include local network connections andremote connections in various embodiments, such that QA system 100 mayoperate in environments of any size, including local and global, e.g.,the Internet. Additionally, QA system 100 serves as a front-end systemthat can make available a variety of knowledge extracted from orrepresented in documents, network-accessible sources and/or structureddata sources. In this manner, some processes populate the knowledgemanager with the knowledge manager also including input interfaces toreceive knowledge requests and respond accordingly.

In one embodiment, a content creator creates content in a document 107for use as part of a corpus of data with QA system 100. The document 107may include any file, text, article, or source of data for use in QAsystem 100. Content users may access QA system 100 via a networkconnection or an Internet connection to the network 102, and may inputquestions to QA system 100, which QA system 100 answers according to thecontent in the corpus of data. As further described below, when aprocess evaluates a given section of a document for semantic content,the process can use a variety of conventions to query it from knowledgemanager 104. One convention is to send a well-formed question.

Semantic data 108 is content based on the relation between signifiers,such as words, phrases, signs, and symbols, and what they stand for,their denotation, or connotation. In other words, semantic data 108 iscontent that interprets an expression, such as by using Natural LanguageProcessing (NLP). In one embodiment, the process sends well-formedquestions (e.g., natural language questions, etc.) to QA system 100 andQA system 100 may interpret the question and provide a response thatincludes one or more answers to the question. In some embodiments, QAsystem 100 may provide a response to users in a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 102. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems shown in FIG. 1 depicts separate nonvolatile data stores (server160 utilizes nonvolatile data store 165, and mainframe computer 170utilizes nonvolatile data store 175. The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 235 to Trusted Platform Module (TPM) 295.Other components often included in Southbridge 235 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 235to nonvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE. 802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 200 and another computer system or device.Optical storage device 290 connects to Southbridge 235 using Serial ATA(SATA) bus 288. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 235to other forms of storage devices, such as hard disk drives. Audiocircuitry 260, such as a sound card, connects to Southbridge 235 via bus258. Audio circuitry 260 also provides functionality such as audioline-in and optical digital audio in port 262, optical digital outputand headphone jack 264, internal speakers 266, and internal microphone268. Ethernet controller 270 connects to Southbridge 235 using a bus,such as the PCI or PCI Express bus. Ethernet controller 270 connectsinformation handling system 200 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIG. 3 is an exemplary diagram depicting the components utilized increating a co-opetition index based on rival behavior in socialnetworks. Organization 300 has competitor/rival data store 310 generatedbased on the organization's profile 320. The profile pertains to theorganization or entity with the organization's interests, activities,areas of interest, action plans, keywords that analysis should take intoaccount, etc. The profile also includes next best competitive actions(NBCAs) utilized by the organization when responding to rival's actionsthat are relevant to the organization's business with the NCBAsincluding the payoff function used by a Bayesian game analysis whenanalyzing the next actions to take by the organization.

Corpus 330 is a body of data collected from online sources. Thesesources include data gathered from social media websites 340, newscontent websites 350, current events content 360, and other onlineingested content. In one embodiment, corpus 330 is utilized by aquestion answering (QA) system that ingests the documents into thecorpus and answers natural language questions pertaining to competitiveactivities found in the corpus that correspond to the organization'scompetitors. Competitor analyzer/identifier module 375 utilizes the datain corpus 330 to identify the organization's competitors and rivals withthis data being stored in data store 310. Competitive activity analyzermodule 380 analyzes the competitive activities pertaining to theorganization's competitors, generates the co-petitive index, andprovides alerts and insights to the user of the system at 390.

FIG. 4 is an exemplary high-level flowchart that performs steps tocreating a co-opetition index based on rival behavior in socialnetworks. FIG. 4 processing commences at 400 and shows the steps takenby a process that performs a social media derived co-opetition analysison behalf of an organization. At step 410, the user of the systemcreates a profile of the organization or entity that is utilizing thesystem. The profile includes the organization's interests, activities,areas of interest, action plans, and keywords that the analysis shouldtake into account, etc. The profile also includes the next bestcompetitive actions (NBCAs) that the organization can take to respond toa competitor's actions relevant for this business with the NCBAsincluding a payoff function that is used when analyzing the next actionsto take. The profile is updated by the organization as needed and storedin data store 320.

At predefined process 420, the process performs the CompetitorAnalyzer/Identifier Module (see FIG. 5 and corresponding text forprocessing details). This routine identifies potential rivals andcompetitors. Identification of rivals and competitors is also based on“conflict of interests” such as in “rival sports teams,” “competingbusinesses,” “rival political parties,” “competing mass media channels,”etc. Predefined process 420 utilizes corpus 330 in identifyingcompetitors. The competitors identified as a result of predefinedprocess 420 are stored in data store 310. At predefined process 430, theprocess performs the Activity Analyzer Module (see FIG. 6 andcorresponding text for processing details). This routine identifiesinteresting information corresponding to the identified competitors. Theanalysis is based on unusual activities, distinct approaches, etc. foundin corpus 330. The results from predefined process 430 are stored indata store 440.

At predefined process 450, the process performs the ActivitySignificance Module (see FIG. 7 and corresponding text for processingdetails). This routine scores the significance of competitor activitiesfound by predefined process 430 and also alerts the user or organizationof activities of concern that were identified by predefined process 450.The competitor activities that are analyzed are retrieved from datastore 440 and the resulting significant competitor activities are storedin data store data store 460. The user or organization retrievessignificant competitor activities from data store 460 as alerts andreports. These alerts and reports help guide the organization's decisionmakers regarding next actions to take with regard to significantcompetitor activities identified by the processing shown in FIG. 4.

FIG. 5 is an exemplary flowchart that performs steps to perform acompetitor analysis and identification module. FIG. 5 processingcommences at 500 and shows the steps taken by a process that analyzesdata to identify and commence analysis of competitors to theorganization. At step 510, the process selects the first set of profiledata pertaining to the organization. The profile data is retrieved fromdata store 320. At step 520, the process formulates the first naturallanguage question based on the selected profile data. An example naturallanguage question for an organization that produces video games mightbe, “what competitors are in the video game industry?” At step 525, theprocess submits the formulated question to question answering (QA)system 100. At step 530, the process receives answers from QA system. Inone embodiment, QA system 100 provides confidence values correspondingto the various responses provided. In this embodiment, the processretains competitors based on the confidence value provided by the QAsystem relating to each competitor. The competitor data received at step530 is stored in data store 540. The process determines as to whethermore natural language questions are to be formulated to identifycompetitors (decision 550). If more natural language questions are to beformulated to identify competitors, then decision 550 branches to the‘yes’ branch which loops back to step 520 to formulate and submit thenext natural language question. This looping continues until there aremore natural language questions to be formulated, at which pointdecision 550 branches to the ‘no’ branch exiting the loop. The processdetermines as to whether there is more profile data from data store 320to process (decision 560). If there is more profile data to process,then decision 560 branches to the ‘yes’ branch which loops back to step510 to select and process the next set of profile data from data store320. This looping continues until there is no more profile data toprocess, at which point decision 560 branches to the ‘no’ branch exitingthe loop.

At step 570, the process analyzes the strength of competitors stored indata store 540 based on the co-opetition index. In one embodiment, theco-petitive index is a scale from one to ten with one being a closebusiness partner (friend) and ten being a strong rival (foe). Thestrength analysis data is stored as strength metadata in data store 575.At step 580, the process eliminates duplicate competitor entries fromdata store 575 and updates the list of the organization's competitors indata store 310. The update includes adding newly found competitors aswell as updating competitors based on the current strength metadatastored in data store 575. At step 590, the users is allowed to edit thecompetitor list and the co-opetition scale data based on personalknowledge of the user, such as a decision maker of the organization.FIG. 5 processing thereafter returns to the calling routine (see FIG. 4)at 595.

FIG. 6 is an exemplary flowchart that performs steps to perform anactivity analyzer module of competitor activities. FIG. 6 processingcommences at 600 and shows the steps taken by a process that analyzesactivities of competitors as gathered from online sources, such associal media sources, and stored in corpus 330. At step 610, the processselects the first competitor from data store 320. At step 620, theprocess formulates the first natural language question for thiscompetitor. Example natural language questions might include “whatproducts or services does this competitor provide?”, “what unusualactivities have been reported for this competitor?”, “what distinctapproaches have been reported for this competitor?”, “what industrystrengths have been reported for this competitor?”, “what areas is thiscompetitor leading in market?”, and the like.

At step 625, the process submits the formulated natural languagequestion to QA system 100. At step 630, the process receives answersfrom QA system. In one embodiment, QA system 100 provides a confidencevalue corresponding to each of the responses. In this embodiment, theprocess retains activity data based on the confidence value of QA systemrelating to each question. The retention might be based by comparing theconfidence value to a threshold. The competitor activity data is storedin data store 640. The process determines as to whether more naturallanguage questions are being formulated to analyze the selectedcompetitor's activities (decision 650). If more natural languagequestions are being formulated to analyze the selected competitor'sactivities, then decision 650 branches to the ‘yes’ branch which loopsback to step 610 to formulate the next question. This looping continuesuntil no more questions are being formulated, at which point decision650 branches to the ‘no’ branch exiting the loop.

At step 660, the process adjusts the selected competitor's co-opetitionindex based on any identified associations of competitor with knownrivals. The associations are identified from the competitor's activitiesstored in data store 640 and the co-petitive index is stored in datastore 670. The process determines as to whether there more competitorsto select and process (decision 680). If there more competitors toselect and process, then decision 680 branches to the ‘yes’ branch whichloops back to step 610 to select and process the next competitor fromdata store 310 as described above. This looping continues until thereare no more competitors to process, at which point decision 680 branchesto the ‘no’ branch exiting the loop. FIG. 6 processing thereafterreturns to the calling routine (see FIG. 4) at 695.

FIG. 7 is an exemplary flowchart that performs steps to perform anactivity significance module based on the identified and analyzedcompetitor activities. FIG. 7 processing commences at 700 and shows thesteps taken by a process that analyzes the significance of identifiedcompetitor activities. At step 710, the process selects the first itemof competitor activity from data store 640. At step 720, the processweights the selected item of competitor activity by the selectedcompetitor's strength retrieved from data store 310 as well as from theorganization's activities and areas of interest retrieved from theorganization's profile stored in data store 320. The weighted competitoractivities are stored in data store 725. The process determines as towhether more competitor activity items to process (decision 730). Ifthere are more competitor activity items, then decision 730 branches tothe ‘yes’ branch which loops back to step 710 to select and process thenext competitor activity from data store 640. This looping continuesuntil there are no more competitor activities to process, at which pointdecision 730 branches to the ‘no’ branch exiting the loop.

At step 735, the process filters the weighted competitor activitiesbased on a threshold value and sorts, or ranks, the remaining weightedcompetitor activities based on the weight associated with theactivities. At step 750, for each competitor activity, the processidentifies a set of possible next business actions to deploy and thecorresponding payoff function in terms of probabilities and amounts. Theidentified next actions are stored in data store 755. At step 760, theprocess performs a Bayesian game theory on the identified next actionsto identify the actions that maximize payoff. In one embodiment, thepayoff is maximized when the co-opetition index of the competitor fallsin the middle of the co-petitive index scale. For example, if the scaleis from one to ten with one being a close business partner (friend) andten being a strong rival (foe), then the maximum payoff would be roughlyan index value of five, halfway on the scale. The analyzed next actionsare stored in data store 770.

At step 775, the process classifies the identified next actions as beingeither competitive or collaborative in nature. The classifications areassociated with the respective next actions and stored in data store770. At step 780, the process adjusts the co-opetition index for thiscompetitor based on the classification of analyzed next actions storedin data store 770. In one embodiment, next actions classified ascompetitive in nature receive positive values moving the index up thescale, while next actions classified as collaborative in nature receivenegative values moving the index down the scale. The competitor'sadjusted co-petitive index value is stored in data store 670. At step790, the process alerts a user in the organization to importantcompetitor activities, identified next actions, co-opetition analysisresults, and competitors based on the co-opetition index. The competitordata is retrieved from data store 310, the competitor's co-petitiveindex is retrieved from data store 670, the filtered and rankedcompetitor activities are retrieved from data store 740, and the nextactions are retrieved from data store 770. FIG. 7 processing thereafterreturns to the calling routine (see FIG. 4) at 795.

While particular embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this disclosure and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this disclosure. Furthermore, it is to be understood that thedisclosure is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to disclosures containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an”;the same holds true for the use in the claims of definite articles.

1. A method implemented by an information handling system that includesa memory and a processor to create a co-opetition index, the methodcomprising: mining data from a plurality of online sources related to aplurality of competitor activities, wherein each of the competitoractivities is associated with a competitor of an organization, whereinthe competitor is selected from a plurality of competitors of theorganization; automatically identifying a plurality of possible businessactions corresponding to at least one of the competitor activities;analyzing the set of possible business actions using a game theoryanalysis, wherein the analyzing results in an identification of one ormore next actions that maximize a payoff; adjusting the co-opetitionindex based on a classification of the next actions on a competitivenessscale; and providing the co-opetition index to a user.
 2. The method ofclaim 1 further comprising: creating a profile of the organization,wherein the profile includes one or more keywords and a list of businessactions that include the plurality of possible business actions; andcomparing the profile to the mined data to identify the plurality ofcompetitors.
 3. The method of claim 1 further comprising: ingesting datafrom the plurality of online sources into a question answering (QA)system corpus, wherein the online sources include one or more socialmedia sources; formulating a plurality of natural languagecompetitive-oriented questions corresponding to the plurality ofcompetitors; submitting the formulated natural languagecompetitive-oriented questions to the QA system after ingestion of thedata; receiving a plurality of responses from the QA system; andobtaining the competitor activities from the plurality of responses. 4.The method of claim 3 further comprising: updating the plurality ofcompetitors based on the obtained competitor activities, wherein theupdating includes analyzing a strength of the competitors based on theco-opetition index; and adjusting the co-opetition index correspondingto a selected one or more of the competitors based on identifiedassociations of the selected competitors with one or more known rivalsof the organization.
 5. The method of claim 1 further comprising:identifying at a new competitor to the organization based on an analysisof the competitor activities; adding the new competitor to the pluralityof competitors of the organization; and alerting the user of the newcompetitor.
 6. The method of claim 1 further comprising: ranking theplurality of competitor activities by a strength value associated withthe respective competitor and a set of profile data associated with theorganization; comparing the ranked plurality of competitor activities toa threshold and filtering the ranked plurality of competitor activitiesbased on the comparison; identifying the plurality of possible businessactions corresponding to the filtered competitor activities; andperforming a Bayesian game theory analysis on each of the identifiedplurality of possible business actions, wherein the analysis results ina payoff value associated with each of the identified plurality ofpossible business actions.
 7. The method of claim 1 further comprising:selecting the next actions based on the maximized payoff, wherein thepayoff is maximized when the co-opetition index of the competitor ishalfway between a minimum and a maximum value on the competitivenessscale.
 8. An information handling system comprising: one or moreprocessors; one or more data stores accessible by at least one of theprocessors; a memory coupled to at least one of the processors; and aset of computer program instructions stored in the memory and executedby at least one of the processors in order to create a co-opetitionindex by performing actions comprising: mining data from a plurality ofonline sources related to a plurality of competitor activities, whereineach of the competitor activities is associated with a competitor of anorganization, wherein the competitor is selected from a plurality ofcompetitors of the organization; automatically identifying a pluralityof possible business actions corresponding to at least one of thecompetitor activities; analyzing the set of possible business actionsusing a game theory analysis, wherein the analyzing results in anidentification of one or more next actions that maximize a payoff;adjusting the co-opetition index based on a classification of the nextactions on a competitiveness scale; and providing the co-opetition indexto a user.
 9. The information handling system of claim 8 wherein theactions further comprise: creating a profile of the organization,wherein the profile includes one or more keywords and a list of businessactions that include the plurality of possible business actions; andcomparing the profile to the mined data to identify the plurality ofcompetitors.
 10. The information handling system of claim 8 wherein theactions further comprise: ingesting data from the plurality of onlinesources into a question answering (QA) system corpus, wherein the onlinesources include one or more social media sources; formulating aplurality of natural language competitive-oriented questionscorresponding to the plurality of competitors; submitting the formulatednatural language competitive-oriented questions to the QA system afteringestion of the data; receiving a plurality of responses from the QAsystem; and obtaining the competitor activities from the plurality ofresponses.
 11. The information handling system of claim 10 wherein theactions further comprise: updating the plurality of competitors based onthe obtained competitor activities, wherein the updating includesanalyzing a strength of the competitors based on the co-opetition index;and adjusting the co-opetition index corresponding to a selected one ormore of the competitors based on identified associations of the selectedcompetitors with one or more known rivals of the organization.
 12. Theinformation handling system of claim 8 wherein the actions furthercomprise: identifying at a new competitor to the organization based onan analysis of the competitor activities; adding the new competitor tothe plurality of competitors of the organization; and alerting the userof the new competitor.
 13. The information handling system of claim 8wherein the actions further comprise: ranking the plurality ofcompetitor activities by a strength value associated with the respectivecompetitor and a set of profile data associated with the organization;comparing the ranked plurality of competitor activities to a thresholdand filtering the ranked plurality of competitor activities based on thecomparison; identifying the plurality of possible business actionscorresponding to the filtered competitor activities; and performing aBayesian game theory analysis on each of the identified plurality ofpossible business actions, wherein the analysis results in a payoffvalue associated with each of the identified plurality of possiblebusiness actions.
 14. The information handling system of claim 8 whereinthe actions further comprise: selecting the next actions based on themaximized payoff, wherein the payoff is maximized when the co-opetitionindex of the competitor is halfway between a minimum and a maximum valueon the competitiveness scale.
 15. A computer program product stored in acomputer readable storage medium, comprising computer program code that,when executed by an information handling system, causes the informationhandling system to create a co-opetition index by performing actionscomprising: mining data from a plurality of online sources related to aplurality of competitor activities, wherein each of the competitoractivities is associated with a competitor of an organization, whereinthe competitor is selected from a plurality of competitors of theorganization; automatically identifying a plurality of possible businessactions corresponding to at least one of the competitor activities;analyzing the set of possible business actions using a game theoryanalysis, wherein the analyzing results in an identification of one ormore next actions that maximize a payoff; adjusting the co-opetitionindex based on a classification of the next actions on a competitivenessscale; and providing the co-opetition index to a user.
 16. The computerprogram product of claim 15 wherein the actions further comprise:creating a profile of the organization, wherein the profile includes oneor more keywords and a list of business actions that include theplurality of possible business actions; and comparing the profile to themined data to identify the plurality of competitors.
 17. The computerprogram product of claim 15 wherein the actions further comprise:ingesting data from the plurality of online sources into a questionanswering (QA) system corpus, wherein the online sources include one ormore social media sources; formulating a plurality of natural languagecompetitive-oriented questions corresponding to the plurality ofcompetitors; submitting the formulated natural languagecompetitive-oriented questions to the QA system after ingestion of thedata; receiving a plurality of responses from the QA system; andobtaining the competitor activities from the plurality of responses. 18.The computer program product of claim 17 wherein the actions furthercomprise: updating the plurality of competitors based on the obtainedcompetitor activities, wherein the updating includes analyzing astrength of the competitors based on the co-opetition index; andadjusting the co-opetition index corresponding to a selected one or moreof the competitors based on identified associations of the selectedcompetitors with one or more known rivals of the organization.
 19. Thecomputer program product of claim 15 wherein the actions furthercomprise: identifying at a new competitor to the organization based onan analysis of the competitor activities; adding the new competitor tothe plurality of competitors of the organization; and alerting the userof the new competitor.
 20. The computer program product of claim 15wherein the actions further comprise: ranking the plurality ofcompetitor activities by a strength value associated with the respectivecompetitor and a set of profile data associated with the organization;comparing the ranked plurality of competitor activities to a thresholdand filtering the ranked plurality of competitor activities based on thecomparison; identifying the plurality of possible business actionscorresponding to the filtered competitor activities; and performing aBayesian game theory analysis on each of the identified plurality ofpossible business actions, wherein the analysis results in a payoffvalue associated with each of the identified plurality of possiblebusiness actions.